Refactor

- renamed many variables
- results better displayed
- fixed log(0) error with 1e-15

src/learning/data.py

@@ -73,7 +73,7 @@ class Dataset:
         self.data = self.data.dropna()
         return self
 
-    def get_dataset(self, test_frac:float=0.15, valid_frac:float=0.15) -> tuple[Data, Data, Data]:
+    def get_dataset(self, test_frac:float=0.2, valid_frac:float=0.2) -> tuple[Data, Data, Data]:
         data = self.data.to_numpy()
         data = np.insert(data, 1, 1, axis=1) # adding bias
         np.random.shuffle(data)
@@ -97,6 +97,43 @@ class Dataset:
             l.append(Data(ds, target))
         return l
 
+class ConfusionMatrix:
+    matrix:np.ndarray
+
+    def __init__(self, dataset_y: np.ndarray, predictions_y:np.ndarray) -> None:
+        classes = len(np.unique(dataset_y))
+        conf_matrix = np.zeros((classes, classes), dtype=int)
+
+        for actual, prediction in zip(dataset_y, predictions_y):
+            conf_matrix[int(actual), int(prediction)] += 1
+        self.matrix = conf_matrix
+
+    def accuracy_per_class(self) -> np.ndarray:
+        return np.diag(self.matrix) / np.sum(self.matrix, axis=1)
+
+    def precision_per_class(self) -> np.ndarray:
+        tp = np.diagonal(self.matrix)
+        fp = np.sum(self.matrix, axis=0) - tp
+        return tp / (tp + fp)
+
+    def recall_per_class(self) -> np.ndarray:
+        tp = np.diagonal(self.matrix)
+        fn = np.sum(self.matrix, axis=1) - tp
+        return tp / (tp + fn)
+
+    def f1_score_per_class(self) -> np.ndarray:
+        prec = self.precision_per_class()
+        rec = self.recall_per_class()
+        return 2 * (prec * rec) / (prec + rec)
+
+    def specificity_per_class(self) -> np.ndarray:
+        total = np.sum(self.matrix)
+        tp = np.diagonal(self.matrix)
+        fp = np.sum(self.matrix, axis=0) - tp
+        fn = np.sum(self.matrix, axis=1) - tp
+        tn = total - (tp + fp + fn)
+        return tn / (tn + fp)
+
 if __name__ == "__main__":
     ds = Dataset("datasets\\classification\\frogs.csv", "Species", TargetType.MultiClassification)
     ds.remove(["Family", "Genus", "RecordID"])